GPU Computing Fundamentals: Parallel Architecture and CUDA Basics

Modern high-performance computing and machine learning rely heavily on massive parallelism powered by GPUs. This text-based course guides you through foundational concepts of heterogeneous computing, clarifying how specialized processors execute intensive workloads alongside traditional CPUs. You will develop a solid theoretical and practical grasp of parallel architectures and standard compute frameworks. What you'll learn: - Understand core architectural differences between CPUs, GPUs, and parallel accelerators - Learn foundational principles of parallel execution, thread hierarchies, and SIMD/SIMT models - Explore memory hierarchies, data transfers, and compute migration between host and device - Examine core programming constructs and workflows in CUDA and OpenCL - Apply basic performance optimization strategies to reduce memory bottlenecks Starting with key hardware terminology and conceptual foundations, the material progresses into practical execution patterns and modern compute strategies. This course is designed for programmers and technical learners who want to understand parallel programming concepts without needing prior experience in low-level accelerator development. Begin reading today to strengthen your understanding of modern GPU computing.